This paper introduces a new construct called Human Factors (HF) Respect as a proposed measure to characterize and quantify assistance provided by humans to autonomous systems. Technological advancements are allowing autonomous systems (or agents) to take on roles previously performed by human teammates. Utilizing autonomous systems as team members might require assistance (or respectful behaviors) from human teammates in order for these autonomous systems to realize their full potential. We believe this forms the basis of the construct of HF Respect, which has not been well studied within the context of human autonomy teaming. We performed a literature review to establish a definition and structure for the HF Respect construct by considering adjacent constructs such as respect seen in human teams, trust, cooperation, and compliance. We defined HF Respect as the opportunity cost (e.g., time) incurred by humans when partnering with autonomous agents to improve the combined team’s performance and assist them in overcoming their limitations. We further defined and categorized this assistance as consideration, cooperation, compliance, and correction. HF Respect and proposed metrics can be used to characterize and quantify assistance to autonomous systems to improve the performance of human-autonomy teams. The construct of HF Respect introduces a new perspective to human-autonomy teaming. Researchers should consider this perspective when investigating human-autonomy teams to better describe human-AI team interactions and to determine if humans who demonstrate high HF Respect improve human-autonomy team performance.